Bücher der Reihe European Association of Methodology Series

This book presents the latest strategies and methodological developments for the analysis of cross-cultural data. Internationally prominent researchers from a variety of fields explain how the methods work and how to apply them.

This book reviews the latest statistical and methodological research on survey research techniques to help improve the reliability of survey generated data. The various methods studied are reviewed along with the resulting empirical evidence and insights gained from the results. The evidence is based on field- or quasi-experimental research, observational or simulation studies or a combination thereof. The book examines: survey modes and response effects, interviewers and survey error, asking sensitive questions, conducting web surveys and access panels, coping with non-response, and handling missing data. This book is ideal for students or professionals who employ survey research methods.

This book reviews the latest statistical and methodological research on survey research techniques to help improve the reliability of survey generated data. The various methods studied are reviewed along with the resulting empirical evidence and insights gained from the results. The evidence is based on field- or quasi-experimental research, observational or simulation studies or a combination thereof. The book examines: survey modes and response effects, interviewers and survey error, asking sensitive questions, conducting web surveys and access panels, coping with non-response, and handling missing data. This book is ideal for students or professionals who employ survey research methods.

This book presents the latest strategies and methodological developments for the analysis of cross-cultural data. Internationally prominent researchers from a variety of fields explain how the methods work and how to apply them.

This unique resource provides guidelines and tools for implementing solutions to issues that arise in small sample research. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology, to psychology, marketing, and economics.

This book summarizes a range of new analytic tools for multitrait-multimethod (MTMM) data. Providing an expository yet accessible approach to cutting-edge developments for MTMM analysis, a selection of quantitative researchers reveal their recent contributions to the field including non-technical summaries and empirical examples.

This book summarizes a range of new analytic tools for multitrait-multimethod (MTMM) data. Providing an expository yet accessible approach to cutting-edge developments for MTMM analysis, a selection of quantitative researchers reveal their recent contributions to the field including non-technical summaries and empirical examples.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches.